class_docstr: A wrapper for the encoder and policy networks of a PPORLModule.
  
  Args:
      encoder_network: The encoder network of the PPORLModule.
      policy_network: The policy network of the PPORLModule.
      distribution_cls: The distribution class to construct with the logits outputed
          by the policy network.
class_docstr_tok: ["A","wrapper","for","the","encoder","and","policy","networks","of","a","PPORLModule",".","Args",":","encoder_network",":","The","encoder","network","of","the","PPORLModule",".","policy_network",":","The","policy","network","of","the","PPORLModule",".","distribution_cls",":","The","distribution","class","to","construct","with","the","logits","outputed","by","the","policy","network","."]
code: def __init__(
      self,
      encoder_network: torch.nn.Module,
      policy_network: torch.nn.Module,
      distribution_cls: torch.distributions.Distribution,
  ):
      super().__init__()
      self.encoder_network = encoder_network
      self.policy_network = policy_network
      self.distribution_cls = distribution_cls
  
file_path: ray/python/ray/rllib/examples/learner/train_w_bc_finetune_w_ppo.py
imports: import gymnasium
  import shutil
  import tempfile
  import torch
  from typing import Mapping
  import ray
  from ray import tune
  from ray.train import RunConfig, FailureConfig
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
  from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
  from ray.rllib.core.models.base import ACTOR, ENCODER_OUT
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
label_desc: Use image node_id 1 to create a new BCActor object from inherited base classes: torch.nn with example: obj = BCActor(encoder_network, policy_network, distribution_cls)